The present invention relates to object detection in medical images, and more particularly, to a regression method for detecting anatomic structures in medical images.
Detecting anatomic structures in medical images, such as ultrasound, X-rays, CT images, MR images, etc., is important for medical image understanding. For example, in order to segment an anatomic structure in a medical image, conventional techniques typically involve anatomic structure detection followed by database-guided segmentation of the anatomic structure. Anatomic structure detection can also provide valuable initialization information for other segmentation techniques such as level set, active contour, etc.
Conventional object detection techniques used to detect anatomic structures in medical images utilize a classifier-based object detection approach. Such a classifier-based object detection approach first trains a binary classifier, discriminating the anatomic structure from the background, and then exhaustively scans the query image for anatomy targets. If the trained classifier is denoted by the posterior probability p(O|I), the scanning procedure mathematically performs one of the following two tasks:find {θ:p(O|l(θ))>0.5;θεΘ}  (1){circumflex over (Θ)}=arg max p(O|I(θ)),  (2)where I(θ) is an image patch parameterized by θ, and Θ is the parameter space where the search is conducted. In (1), multiple objects are detected, and in (2), one object is detected.
The above described conventional approach reaches real time performance for detecting objects in non-medical images. In, P. Viola et al., “Rapid Object detection Using a Boosted Cascade of Simple Features,” In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 511-518, 2001, which is incorporated herein by reference, the classifier-based approach is used for real time frontal view face detection by exhaustively searching all possible translations and a sparse set of scales. Other approaches that detect objects under in-plane/out-of-plane rotations have been proposed, but only a sparse set of orientations and scales are tested in order to meet real time requirement. In such approaches, either multiple classifiers are learned, or one classifier is learned but multiple integral images according to different rotations are computed. In general, the computational complexity of the classifier-based approach linearly depends on the image size (for the translation parameter) and the number of tested orientations and scales.
Medical anatomy often manifests an arbitrary orientation and scale within medical images. In order to perform subsequent tasks, such as object segmentation, an accurate representation of orientation and scale may be required. Accordingly, detection speed must be sacrificed for testing a dense set of orientations and scales when using conventional detection approaches. Further, if the one classifier approach, which has been shown to perform better than the multiple-classifier approach, is used, then rotating the images and computing their associated integral images cost extra computations. Therefore, due to the exhaustive nature of the classifier-based approach, it is challenging to build a rapid detector for medical anatomy using the classifier-based approach.